Improved Roadside Monocular View Small Target Detection Algorithm Based on YOLOv5
Aiming at the problems of low recognition accuracy and fewer features of long-distance targets and small targets in the roadside view under vehicle-road cooperative sensing traffic scenarios,an improved algorithm for small target detection based on YOLOv5 is proposed.Firstly,in the backbone network,the GAM attention module is added to enhance the feature extraction ability of the network.Secondly,RepBi-PAN is introduced to replace the PANet structure of the original neck network to in-crease the network's ability to localize small targets.Finally,the use of SIoU loss function instead of the original CIoU loss func-tion can effectively avoid the arbitrary matching of the prediction frames in the regression process,thus enhancing the robustness of the model and accelerating the training speed of the network model.The experimental results show that compared with the origi-nal YOLOv5 6.0 version,the average accuracy mAP of each category is improved by 6.9 percentage points when the intersection over union IoU is 0.5,and the average accuracy mAP of each category is improved by 6.4 percentage points when the intersection over union IoU is 0.95,which effectively improves the detection capability of small target detection in the road-side view.
万方数据
维普
